40 citations · 85 across the 24 of their papers we have counts for
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cs.AI2026
CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation
Chenlu Ding, Jiancan Wu, Yanchen Luo +4
Multimodal large language models (MLLMs) can reproduce sensitive, copyrighted, or unsafe information from their training data. Effective unlearning must remove target information w…
cs.AI2024
Unified Parameter-Efficient Unlearning for LLMs
Chenlu Ding, Jiancan Wu, Yancheng Yuan +5
The advent of Large Language Models (LLMs) has revolutionized natural language processing, enabling advanced understanding and reasoning capabilities across a variety of tasks. Fin…
cs.AI2024★ 1 cited
-DPO: Direct Preference Optimization with Dynamic
Junkang Wu, Yuexiang Xie, Zhengyi Yang +5
Direct Preference Optimization (DPO) has emerged as a compelling approach for training Large Language Models (LLMs) to adhere to human preferences. However, the performance of DPO…